written by Paola Alunni (Editor Manager at ByTek, part of Datrix group)
Monitor, but above all measure the value of customers over time: Customer Lifetime Value (CLV) is a fundamental indicator for the optimization of marketing strategies.
Today, the success or failure of a strategy, and consequently of the company that implements it, is entrusted to long-term objectives and metrics. To do this, it is important to quantify, evaluate and monitor the opportunities that Customer Lifetime Value offers.
It is difficult to estimate and analyze the CLV, because user behavior is constantly changing, but it is fundamental for company performances. Knowing your audience dynamically, addressing and interacting only with those who are really interested in your offer: this is the only way a company can grow successfully.
Let’s go through the main points to know:
What is customer lifetime value?
We can define Customer Lifetime Value has the total value customer will have over time. In a marketing strategy, It means that Customer Lifetime Value is an indicator of profits affected by the time for which user remains a customer of the company.
The Customer Lifetime Value helps to calculate how much a company has had to invest to acquire a customer, how much to make them buy products or services over time, but above all the cost of loyalty. Consequently, the CLV analysis allows you to reach customers who are really interested in your proposal and establish a relationship of trust with them, a relationship that should be kept constant over time.
If you don’t target the right customers, i.e. the one that is really interested in your services or products, you can’t put in place some really effective marketing strategies.
Se non ci si rivolge ai clienti giusti, cioè realmente interessati ai propri servizi o prodotti, non si può mettere in atto alcune strategie di marketing realmente efficace.
Why is CLV such an important metric?
The CLV is a performance indicator, a crucial analysis tool since it is able to analyze various factors at the same time, providing for example important data to better target advertising campaigns on the various channels. Once concrete objectives have been established and planned, the CLV allows you to set the campaign budget correctly. To do this, however, it is essential to establish a relationship with the user-customer. Interacting and communicating correctly with him allows you to design targeted marketing strategies and to offer customized solutions to meet specific needs. This is true as long as you collect the right data and calculate the CLV correctly, which is not so easy.
But how much does it cost to acquire a new customer and how much to retain him? In other words, how should you measure Customer Lifetime Value?
How to measure CLV?
Intercepting the customer’s needs, communicating with him and bringing him to purchase a service or product is an operation that has a cost. During customer’s life, for the entire duration of his relationship with the company, he will make purchases; the CLV measures the profits generated by individual leads, in consideration of their purchasing behavior.
Measuring the Customer Lifetime Value needs to perform predictive analysis, such as the ability to estimate how much a customer will spend over time in relation to the company’s costs. It is difficult for a company to reach customers interested in its products or services without a calculation of the CLV. In this case, it is impossible to compete with other marketers. How to calculate this value, actually?
It is necessary to keep in mind some variables such as:
- the customer acquisition cost (CAC)
- the retention rate.
- the dropout rate.
- recurring purchases.
Discover the solution to ACTIVATE your data
CLV and cost of acquisition (CAC): find the right balance.
Along with Customer Lifetime Value, another key metric for a marketing strategy is the Cost of Acquisition. Those who have the confidence and the ability to measure these two indicators are able to predict how their business will go. You can predict how much it will cost to acquire a new customer and how much he will be willing to spend during his relationship with the company. But above all, you will be able to evaluate the relationship between Acquisition Cost and Customer Lifetime Value: the higher the first in respect to the second, the most your strategy would certainly have to be rethought, in order to maximize ROI, as well.
The importance of first-party data in calculating the CLV.
In measuring Customer Lifetime Value and Acquisition Cost, first-party data plays a fundamental role. Knowing how to integrate and analyze them allows you not only to quantify the CLV but also to provide personalized, tailor-made answers. This is true if and only if the company knows how to make the best use of proprietary data.
Data that the company is able to obtain from its channels (digital or traditional ones) represent in fact a fundamental resource for the purpose of measuring the Customer Lifetime Value. These data are collected in compliance with the privacy legislation and, at the same time, they give you the possibility to personalize marketing strategy.
To do this, it is necessary to overcome the logic of internal silos and organize a centralized data lake where all the information collected through proprietary sources converge. This is a huge amount of data that must be extracted and transformed into a resource. To make the best from these data, human intelligence must necessarily be assisted by artificial intelligence, AI.
3rdPlace uses Artificial Intelligence technologies capable of analyzing a huge amount of data by correlating them with each other. Our company can collect in a data lake, integrate, and analyze the information contained in the first-party data, thanks to proprietary technologies.
DataLysm is the customer data platform developed by 3rdPlace that allows you to organize effective and highly personalized marketing strategies. All this thanks to the potential of first-party data, a huge amount of information, which is extracted and used thanks to Artificial Intelligence algorithms. The work carried out by DataLysm begins with an in-depth knowledge of users and continues with a segmentation into clusters, to identify and then activate strategic paths suitable for generating business value.